The week's pick
Random Articles
Reseach Article
Application of Big Data in Libraries
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 16 |
| Year of Publication: 2019 |
| Authors: Washington Kamupunga, Yang Chunting |
10.5120/ijca2019918955
|
Washington Kamupunga, Yang Chunting . Application of Big Data in Libraries. International Journal of Computer Applications. 178, 16 ( Jun 2019), 34-38. DOI=10.5120/ijca2019918955
Abstract
In the dynamic environment, records and management systems are independently maintained by education institutions, libraries and books whilst data are not readily accessible in a centralized position. Big data is being created due to digitalization of libraries and this has imposed limitations to researchers, educationists, scholars and policy maker’s efforts in improving the quality and efficiency. As a result, serving the users with books and articles that are in line with their interests is a great challenge. This paper addresses the issues of bringing various sources of information from different sources and institutions into one place in real time which can be time saving. The primary objective is to decrease the time that lapses between searching the reading material and the actual reading. Thus, a mechanism by which this bridge can be gapped is of paramount importance as access to information is costly especially to those with limited internet access. The research focuses on the development of a strategy that reduces time of finding reading material and this is in line with current recommendation system. Through this system there is great analyses of book descriptions to identify books that are in line with users’ interests. Within time huge amount of data is collected from the researchers, educationists, scholars and policy makers and this big data will be used to train machines to automate the tasks to some extent. As a result, the valuable information gained from analyzing massive amounts of aggregated libraries data can provide key insights in improving information accessibility. This makes researchers, educationists, scholars and policy maker’s reach out for research solutions easily and cheaply and also makes information more accessible to the underprivileged and marginalized.
References
- Lindell, Jim. (2018). What are Big Data and Analytics?. 10.1002/9781119512356.ch1.
- Singh, Pramod. (2019). Recommender Systems: With Natural Language Processing and Recommender Systems. 10.1007/978-1-4842-4131-8_7.
- Yu. Ignat’ev, V & Lemtyuzhnikova, Daria & I. Rul’, D & L. Ryabov, I. (2018). Constructing a Hybrid Recommender System. Journal of Computer and Systems Sciences International. 57. 921-926. 10.1134/S1064230718060060.
- Lauren Reinhalter, and Rachel J. Wittmann. "The Library: Big Data’s Boomtown." Serials Librarian 67.4(2014):363-372.
- Daniel K.B. (ed.). (2017). Big Data and Learning Analytics in Higher Education, New Zealand.
- DOI: http://arxiv.org/pdf/1203.0160v2.pdf
- Qian, Y. H., Cheng, H. H., Liang, X. Y., & Wang, J. X. (2015). Review for variable asso- ciation measures in big data. Journal of Data Acquisition & Processing, 6, 1147–1159.
- Wu, K., Su, X. N., & Deng, S. H. (2013). Big data, cloud computing and user behavior analysis. Digital Library Forum. 6. Digital Library Forum (pp. 19–23).
- Zhang, H. (2016). Analysis on the present situation of big data research in university libraries in China. Library Work and Study, 7, 46–50
- Coelho, H. S. (2011). Web 2.0 in academic libraries in Portuguese public universities: A longitudinal study. Libri, 61(4), 249–257.
- Li, L. (2012). Discussion on personalized service mode of digital library in colleges and universities. Lantai World, 23, 91–92.
- Gu, L. P. (2010). Research on models of user behavior driven personalized services. New Technology of Library and Information Service, 26(10), 1–9.
- Ferran, N., Mor, E., & Minguillón, J. (2005). Towards personalization in digital libraries through ontologies. Library Management, 26(4/5), 206–217 (12).
- Li, B. Y., & Zhang, X. Y. (2013). The problem of large data in the construction of digital library. Information Sciences, 11, 26–29.
- Li, Shuqing & Jiao, Fusen & Zhang, Yong & Xu, Xia. (2019). Problems and Changes in Digital Libraries in the Age of Big Data From the Perspective of User Services. The Journal of Academic Librarianship. 45. 22-30. 10.1016/j.acalib.2018.11.012.
- Audrey, W. (2012). Strata Week: Harvard Library releases big data for its books. Retrieved from http://radar.oreilly.com/2012/04/harvard-book-data-cloudera- hadoop-splunk-ipo.html.
- Huang, Y., Lu, W., Cheng, Q. K., & Gui, S. S. (2016). The structure function recognition of academic text—Application in academic search. Journal of the China Society for Scientific and Technical Information, 35(4), 425–431. Information Technology Journal, 5(3), 590–600. DOI: http://dx.doi.org/10.3923/itj.2006.590.600
- Zhang, X. L. (2001). Mechanisms of digital library: Evolution of paradigms and its challenges. Journal of the Library Science in China, 27(6), 3–8.
- Bansal, Ankita & Jain, Roopal & Modi, Kanika. (2019). Big Data Streaming with Spark. 10.1007/978-981-13-0550-4_2.
- Bikakis, Nikos. "Big Data Visualization Tools." (2018).
- Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Waltham, MA: Elsevier.
- Sumathi, S., & Sivanandam, S. N. (2006). Introduction to data mining and its applications. Berlin: Springer.
- Girija, N., & Srivatsa, S. K. (2006). A research study: Using data mining in knowledge base business strategies.
- Siguenza-Guzman, L., Saquicela, V., Avila-Ordóñez, E., Vandewalle, J., & Cattrysse, D. (2015). Literature review of data mining applications in academic libraries. The Journal of Academic Librarianship, 41(4), 499–510.
- Bu, Y., Brokar, V., Carey, M. J., Rosen, J., Polyzotis, N., Condie, T., … Ramakrishnan R. (2012). Scaling datalog for machine learning on Big Data. Computer research repository (CoRR) (pp. 1–14). Cornell University Library.
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big Data: The next frontier for innovation, competition and productivity. New York, NY: McKinsey Global Institute.
- Kalota, Faisal. (2015). Applications of Big Data in Education. International Journal of Social, Education, Economics and Management Engineering. 9. Kalota, F. (2015). 'Applications of Big Data in Education'. World Academy of Science, Engineering and Technology, International Science Index 101, International Journal of Social, Education, Economics and Management Engineering, 9(5), 1501 - 1506.
- Midgley, Gerald & Lindhult, Erik. (2019). What is Systemic Innovation?.
Index Terms
Keywords